code: def test_predict(dataset_ncf_files_sorted, model_type):
      # test data format
      train_path, test_path, _ = dataset_ncf_files_sorted
      test = pd.read_csv(test_path)
      data = Dataset(
          train_file=train_path,
          test_file=test_path,
          n_neg=N_NEG,
          n_neg_test=N_NEG_TEST,
      )
      model = NCF(
          n_users=data.n_users,
          n_items=data.n_items,
          model_type=model_type,
          n_epochs=1,
      )
      model.fit(data)
  
      test_users, test_items = list(test[DEFAULT_USER_COL]), list(
          test[DEFAULT_ITEM_COL]
      )
  
      assert type(model.predict(test_users[0], test_items[0])) == float
  
      res = model.predict(test_users, test_items, is_list=True)
  
      assert type(res) == list
      assert len(res) == len(test)
  
file_path: recommenders/tests/unit/recommenders/models/test_ncf_singlenode.py
imports: import os
  import shutil
  import pytest
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling a global function with example usage: test_predict(dataset_ncf_files_sorted, model_type) without return types